Papers
7
Total Citations
458
H-Index
6
About
Xichen Shi is a pioneering roboticist whose research sits at the intersection of bio-inspired flight, agile autonomy, and learning-based control for aerial vehicles. His most celebrated work, *Neural-Fly* (2022, 232 citations), revolutionizes drone flight by enabling rapid, online learning that allows UAVs to execute safe and precise maneuvers in dynamic, high-speed winds—a critical step toward commoditizing drones for real-world use. Earlier, Shi made foundational contributions to high-speed navigation with his work on motion primitives and 3D path planning for fast flight through dense forests (2015, 85 citations), providing the theoretical framework for agile obstacle avoidance. He is also the driving force behind the Bat Bot (B2) project (2016, 80 citations), a biologically inspired flying machine that models the complex, articulated-wing flight of bats. Through Lagrangian modeling and nonlinear control synthesis, Shi developed systematic controllers for B2, offering unprecedented insight into vertebrate flight dynamics. His work on Neural-Swarm2 (2020) further extends his impact into heterogeneous multirotor swarms, using learned interactions to enable safe, close-proximity flight. With over 450 total citations, Shi’s research bridges theory and practice, advancing both the science of flight and the engineering of next-generation autonomous drones.
Research Focus
Key Achievements
Top Papers
- 1Neural-Fly enables rapid learning for agile flight in strong winds232 citations · 2022
- 2Motion primitives and 3D path planning for fast flight through a forest85 citations · 2015
- 3Bat Bot (B2), a biologically inspired flying machine80 citations · 2016
- 4Lagrangian modeling and flight control of articulated-winged bat robot31 citations · 2015
- 5Motion primitives and 3-D path planning for fast flight through a forest15 citations · 2013
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